Johannes Geyer <[email protected]>:
The most powerful way to compare predictions at different X values is
to add "fake" observations to the end of your data, and -predict- y
for those obs.
You can add four obs with
g byte real=1
set obs `=_N+4'
and -replace- each variable in X as appropriate, or
edit x* age* in `=_N-3'/`=_N'
to type them in the Data Editor. If it is tedious to calculate all the
X variables from age and other primitives, you can put all those
calculations inside a little program to be run both on your real data
and any fake data you may append later. Then
predict yhat
li yhat x* age* in `=_N-3'/`=_N'
to see the predictions. You will want to drop the obs after you're
done listing, graphing, whatever, so that you don't include as real
data in a subsequent regression. You can
drop in `=_N-3'/`=_N'
but I find starting with making an indicator of real data safer and
easier, in which case you can just
drop if mi(real)
drop real
On 9/7/07, Johannes Geyer <[email protected]> wrote:
> Dear all,
>
> I would like to estimate a linear model with several interactions:
>
> reg y age age2 age3 age4 x1 x2 x3 x4 age*x1 age*x2 .... age4*x3 age4*x4
>
> Then I would like to compare results for different x, i.e. I have four
> groups (and a base category) and they may differ in their age-profile. So
> far I used Excel to calculate different profiles but I it is very
> cumbersomely... That is why I am looking for a more convenient
> post-estimation command. (I think the spost package does something similar
> for categorical variables.) The problem arises because I do not want to
> compare at sample means.
>
> I would be happy if somebody had an idea which command could help,
>
> thank you
>
> regards
>
> Johannes
>
>
>
>
>
>
>
> ----------------------
> Johannes Geyer
> Deutsches Institut f�r Wirtschaftsforschung (DIW Berlin)
> German Institute for Economic Research
> Graduate Center
> DIW Berlin
> Mohrenstra�e 58
> 10117 Berlin
> Tel: +49-30-89789-258
>
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